ArticleSensors (Basel, Switzerland)2021
Detecting Parkinson's Disease from Wrist-Worn Accelerometry in the U.K. Biobank.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Movement Disorders and Smart Wrist Devices: A Comprehensive Study.Sensors (Basel, Switzerland) · 2025Pooled it
- Devices for remote continuous monitoring of people with Parkinson's disease: a systematic review and cost-effectiveness analysis.Health technology assessment (Winchester, England) · 2024Pooled it
- Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Real-world gait changes preceding Parkinson's disease diagnosis in a population-scale UK biobank cohort.Journal of neural transmission (Vienna, Austria : 1996) · 2026Article
- Multi-Task Wearable Parkinson's Disease Detection with a Pretrained Spatio-Temporal Graph Encoder and Task-Level Token Aggregation.Bioengineering (Basel, Switzerland) · 2026Article
- Harnessing artificial intelligence: Revolutionizing clinical care for Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Association between wrist-worn actigraphy and the MDS-UPDRS Parkinson's disease rating scale through machine learning: an exploratory study.Frontiers in digital health · 2026Article
- Joint Bayesian Hidden Markov Model With Subject-Specific Transitions for Wearable Sensor Data.Statistics in medicine · 2025Article
- Accelerometry is a valid method to distinguish between healthy and 6-OHDA-lesioned parkinsonian rats.Scientific reports · 2025Article
- Estimating motor symptom presence and severity in Parkinson's disease from wrist accelerometer time series using ROCKET and InceptionTime.Scientific reports · 2025Article
- Multi-modal machine learning approach for early detection of neurodegenerative diseases leveraging brain MRI and wearable sensor data.PLOS digital health · 2025Article
- Machine Learning in the Parkinson's disease smartwatch (PADS) dataset.NPJ Parkinson's disease · 2024Article
- Integrating Big Data, Artificial Intelligence, and motion analysis for emerging precision medicine applications in Parkinson's Disease.Journal of big data · 2024Article
- Evaluating the prediction performance of objective physical activity measures for incident Parkinson's disease in the UK Biobank.Journal of neurology · 2023Article
- Gait Monitoring and Analysis: A Mathematical Approach.Sensors (Basel, Switzerland) · 2023Article
- Wearable movement-tracking data identify Parkinson's disease years before clinical diagnosis.Nature medicine · 2023Article
- Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review.Sensors (Basel, Switzerland) · 2023Review
- Detection of Parkinson's Disease Using Wrist Accelerometer Data and Passive Monitoring.Sensors (Basel, Switzerland) · 2022Article
- Clustering Accelerometer Activity Patterns from the UK Biobank Cohort.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) is a chronic movement disorder that produces a variety of characteristic movement abnormalities. The ubiquity of wrist-worn accelerometry suggests a possible sensor modality for early detection of PD symptoms and subsequent tracking of PD symptom severity. As an initial proof of concept for this technological approach, we analyzed the U.K. Biobank data set, consisting of one week of wrist-worn accelerometry from a population with a PD primary diagnosis and an age-matched healthy control population. Measures of movement dispersion were extracted from automatically segmented gait data, and measures of movement dimensionality were extracted from automatically segmented low-movement data. Using machine learning classifiers applied to one week of data, PD was detected with an area under the curve (AUC) of 0.69 on gait data, AUC = 0.84 on low-movement data, and AUC = 0.85 on a fusion of both activities. It was also found that classification accuracy steadily improved across the one-week data collection, suggesting that higher accuracy could be achievable from a longer data collection. These results suggest the viability of using a low-cost and easy-to-use activity sensor for detecting movement abnormalities due to PD and motivate further research on early PD detection and tracking of PD symptom severity.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.